PD4781 Sample Summary

## `summarise()` has grouped output by 'patient', 'age_at_sample_exact', 'age_at_sample', 'DOB', 'DATE_OF_DIAGNOSIS'. You can override using the `.groups` argument.
## Joining, by = "PDID"
patient ID age_at_sample_exact cell_type phase BaitLabel
3 PD4781 PD4781f 81.77139 PB Gran Recapture PD4781f
4 PD4781 PD4781g 82.02053 PB Gran Recapture PD4781g
5 PD4781 PD4781h 83.36208 PB Gran Recapture PD4781h
1 PD4781 COLONY84 83.81930 BFU-E-Colony Colony NA
6 PD4781 PD4781i 85.58522 PB Gran Recapture PD4781i
2 PD4781 COLONY86 86.02327 BFU-E-Colony Colony NA

Tree

tree=plot_basic_tree(PD$pdx,label = PD$patient,style="classic")

Expanded Tree with Node Labels

The nodes in this plot can be cross-referenced with nodes specified in subsequent results. The plot also serves to give an idea of what the topology at the top of the tree looks like.

tree=plot_basic_tree(expand_short_branches(PD$pdx,prop = 0.1),label = PD$patient,style="classic")
node_labels(tree)

Timing of driver mutations (using Model = poisson_tree )

Note that the different colours on the tree indicate the separately fitted mutation rate clades.

Driver Specific Mutation Rates & Telomere Lengths by Colony & Timepoint

## 
## Random-Effects Model (k = 1; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  -0.0000    0.0000    4.0000      -Inf   16.0000   
## 
## tau^2 (estimated amount of total heterogeneity): 0
## tau (square root of estimated tau^2 value):      0
## I^2 (total heterogeneity / total variability):   0.00%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  15.2797  0.8644  17.6760  <.0001  13.5855  16.9740  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
node driver status child_count type colony_count mean_lambda_rescaled correction sd_rescaled lb_rescaled ub_rescaled median_rescaled p_lt_wt
-1 WT 1 -1 local 1 15.27972 1.0798 0.7472428 13.86324 16.79247 15.26507 NA
66 TET2,JAK2,9pUPD,7p-,7q- 1 56 local 56 23.96258 1.0798 2.7250445 19.04924 29.48395 23.87934 0.000050
120 DNMT3A,PPM1D,JAK2 1 4 local 4 20.21356 1.0798 1.9553184 17.74161 25.14174 19.73444 0.000625

Driver Acquisition Timeline

All ages are in terms of post conception years. The vertical red lines denote when colonies were sampled and blue lines when targeted follow up samples were taken.

patient node driver child_count lower_median upper_median lower_lb95 lower_ub95 upper_lb95 upper_ub95 N group age_at_diagnosis_pcy max_age_at_sample min_age_at_sample
PD4781 120 DNMT3A,PPM1D,JAK2 4 0.025686 55.83527 0.0181318 0.0472691 52.18561 62.46789 6 JAK2 74.18207 86.75154 82.49966
PD4781 66 TET2,JAK2,9pUPD,7p-,7q- 56 0.025686 67.47880 0.0181318 0.0472691 62.94302 70.86795 6 JAK2 74.18207 86.75154 82.49966

Copy Number Variation and Timing

Summary of LOH timing inference

## Timings using the Clade Specific Rates
label node het.sensitivity chr start end nhet nhom mean_loh_event lower_loh_event upper_loh_event t_before_end t_before_end_lower t_before_end_upper kb count_in_bin count_se pmut pmut_se xmean xse_mean xsd x2.5. x50. x97.5. xn_eff xRhat lmean lse_mean patient driver3 child_count
9pUPD 66 0.9897 9 41634 20134580 1 5 54.89 35.94 65.62 12.42 1.689 31.37 20100000 3508 59.23 0.007661 0.0001294 0.8154 0.000725 0.1163 0.53373 0.8369 0.9749 25752 0.9999 7.927 0.000881 PD4781 TET2,JAK2,9pUPD,7p-,7q- 56
9pUPD_B 122 0.9398 9 41634 33877116 2 1 NA NA NA NA NA NA 33800000 6732 82.05 0.014702 0.0001792 0.4746 0.001300 0.2054 0.09164 0.4749 0.8525 24965 0.9999 4.223 0.000318 NA NA NA

Duplications?

VAF Distribution of Targeted Follow Up Samples

Here we exclude all local CNAs and depict as color VAF plots